Device	Data returned

Service provider ID	0482735
Dispensing Device ID	A263554
Operator ID	8936252
Work Order #	7628735
Material Type	Brand XYZ Liquid Pesticide
Timestamp data of processing	12-Jul-2010; 09:35:15.2
unit 210
Actuation system 216 status	ON
Geo-location data of location	35° 43′ 34.52″ N, 78° 49′ 46.48″W
tracking system
310
Temperature data of	73 degreesF.
temperature sensor
312
Humidity data of humidity	32%
sensor
314
Light data oflight sensor 316	4.3 volts
Heading data of electronic	213degrees
compass
318
Inclinometer data of	−40
inclinometer 320
Accelerometer data of	0.285g
accelerometer
322
Surface temperature data	79 degrees F.
ofIR sensor 324
Distance data of sonar	6.3inches
range finder
326
IMU data ofIMU 328	Accelerometer = 0.285 g,
	Angular acceleration = +52 degrees/sec,
	Magnetic Field = −23 micro Teslas (uT)
Surface type	Grass
Material level in tank	¾ full
Tank operating pressure	27 psi

TABLE 2

Example record ofdevice data 222 of
enhancedmobile dispensing device 100

Device	Data returned

Service provider ID	0482735
Dispensing Device ID	A263554
Operator ID	8936252
Work Order #	7628735
Material Type	Brand XYZ Liquid Pesticide
Timestamp data of processing	12-Jul-2010; 09:35:19.7
unit 210
Actuation system 216 status	ON
Geo-location data of location	35° 43′ 34.49″ N, 78° 49′ 46.53″W
tracking system
310
Temperature data of	73 degreesF.
temperature sensor
312
Humidity data of humidity	31%
sensor
314
Light data oflight sensor 316	4.3 volts
Heading data of electronic	215degrees
compass
318
Inclinometer data of	−37
inclinometer 320
Accelerometer data of	0.271g
accelerometer
322
Surface temperature data	79 degrees F.
ofIR sensor 324
Distance data of sonar	5.9inches
range finder
326
IMU data ofIMU 328	Accelerometer = 0.271 g,
	Angular acceleration = +131 degrees/sec,
	Magnetic Field = −45 micro Teslas (uT)
Surface type	Grass
Material level in tank	¾ full
Tank operating pressure	31 psi

The electronic records created by use of enhancedmobile dispensing device100 include at least the date, time, and geographic location of dispensing operations. Referring again to Tables 1 and 2, other information about dispensing operations may be determined by analyzing multiple records ofdevice data222. For example, the total onsite-time with respect to awork order224 may be determined, the total number of actuations with respect to awork order224 may be determined, the total spray coverage area with respect to awork order224 may be determined, and the like. Individual records ofdevice data222, such as shown in Tables 1 and 2, as well as any aggregation of multiple records ofdevice data222 of enhancedmobile dispensing device100 for forming any useful conclusions about dispensing operations are examples of electronic records of dispensing operations for which there is no observable way of knowing whether service has been performed.

Additionally, timestamped and geo-stamped digital images that are captured usingimage capture device330 may be stored and associated with certain records ofdevice data222. In one example,image capture device330 may be used to capture landmark and/or non-dispensing event during dispensing operations. For example, in an insect extermination application, along with dispensing material from enhancedmobile dispensing device100, the user may be performing other non-dispensing activities, such as installing a termite spike at certain locations. In this example,image capture device330 may be used to capture a timestamped and geo-stamped digital image of the termite spike when installed. In this way, an electronic record of this activity is stored along with the information in, for example, Tables 1 and 2. In this example,image capture device330 may be triggered manually by the user via controls of user interface130. Further, calibration and/or device health information may be stored along with the information in, for example, Tables 1 and 2.

Referring toFIG. 4, a perspective view of an example of enhancedmobile dispensing device100 that includes imaging equipment and software for performing optical flow-based dead reckoning and other processes is presented. In this example, enhanced mobile dispensing device100 (e.g., an enhanced dispensing wand) includes a camera system.410 andcontrol electronics412 that includes certain image analysis software for supporting the optical flow-based dead reckoning and other processes. More details ofcontrol electronics412 supporting the optical flow-based dead reckoning and other processes are described with reference toFIG. 5.

Thecamera system410 may include any standard digital video cameras that have a frame rate and resolution that is suitable, preferably optimal, for use in enhancedmobile dispensing device100. Each digital video camera may be a universal serial bus (USB) digital video camera. In one example, each digital video camera may be the Sony PlayStation®Eye video camera that has a 10-inch focal length and is capable of capturing 60 frames/second, where each frame is, for example, 640×480 pixels. In this example, the optimal placement of at least one digital video camera on enhancedmobile dispensing device100 is nearspray nozzle116 and is about 10 to 13 inches from the surface to be sprayed, when in use. This mounting position is important for two reasons: (1) so that the motion of at least one digital video camera tracks with the motion of the tip of enhancedmobile dispensing device100 when dispensingspray material120, and (2) so that some portion of the surface being sprayed is in the field of view (FOV) of at least one digital video camera.

In an alternative embodiment, the camera system may include one or more optical flow chips. The optical flow chip may include an image acquisition device and may measure changes in position of the chip (i.e., as mounted on the marking device) by optically acquiring sequential images and mathematically determining the direction and magnitude of movement. Exemplary optical flow chips may acquire images at up to 6400 times per second at a maximum of 1600 counts per inch (cpi), at speeds up to 40 inches per second (ips) and acceleration up to 15 g. The optical flow chip may operate in one of two modes: 1) gray tone mode, in which the images are acquired as gray tone images, and 2) color mode, in which the images are acquired as color images. In some embodiments, the optical flow chip may be used to provide information relating to whether the marking device is in motion or not.

In an exemplary implementation based on a camera system including an optical flow chip, the one or more optical flow chips may be selected as the ADNS-3080 chip available from Avago Technologies (e.g., see http://www.avagotech.com/pages/en/navigation_interface_devices/navigation_sensors/led-based_sensors/adns-3080/).

In one example, the digital output of thecamera system410 may be stored in any standard or proprietary video file format (e.g., Audio Video Interleave (.AVI) format and QuickTime (.QT) format). In another example, only certain frames of the digital output of thecamera system410 may be stored.

Referring toFIG. 5, a functional block diagram of an example ofcontrol electronics412 for supporting the optical flow-based dead reckoning and other processes of enhancedmobile dispensing device100 ofFIG. 4 is presented. Dead reckoning is the process of estimating an object's current position based upon a previously determined position, and advancing that position based upon known or estimated speeds over elapsed time, and based upon direction. The optical flow-based dead reckoning that is incorporated in enhancedmobile dispensing device100 of the present disclosure is useful for determining and recording the apparent motion of the device during dispensing operations and, thereby, track and log the movement that occurs during dispensing operations. For example, upon arrival at the job site, a user may activate thecamera system410 and the optical flow-based dead reckoning process of enhancedmobile dispensing device100. A starting position, such as GPS latitude and longitude coordinates, is captured at the beginning of the dispensing operation. The optical flow-based dead reckoning process is performed throughout the duration of the dispensing operation with respect to the starting position. Upon completion of the dispensing operation, the output of the optical flow-based dead reckoning process, which indicates the apparent motion of the device throughout the dispensing operation, is saved in the electronic records of the dispensing operation.

Control electronics

412 is substantially the same ascontrol electronics132 ofFIGS. 1A,1B,2, and3, except that it further includes certainimage analysis software510 for supporting the optical flow-based dead reckoning and other processes of enhancedmobile dispensing device100.Image analysis software510 may be any image analysis software for processing the digital video output from thecamera system410.Image analysis software510 may include, for example, anoptical flow algorithm512, which is the algorithm for performing the optical flow-based dead reckoning process of enhancedmobile dispensing device100.

FIG. 5 also shows acamera system410 connected to controlelectronics412 of enhancedmobile dispensing device100. In particular, image data514 (e.g., .AVI and .QT file format, individual frames) of at least one digital video camera is passed toprocessing unit210 and processed byimage analysis software510. Further,image data514 may be stored inlocal memory212.

Optical flow algorithm

512 ofimage analysis software510 is used for performing an optical flow calculation for determining the pattern of apparent motion of acamera system410, thereby, determining the pattern of apparent motion of enhancedmobile dispensing device100. In one example,optical flow algorithm512 may use the Pyramidal Lucas-Kanade method for performing the optical flow calculation. An optical flow calculation is the process of indentifying unique features (or groups of features) in common to at least two frames of image data (e.g., frames of image data514) and, therefore, can be tracked from frame to frame. Thenoptical flow algorithm512 compares the xy position (in pixels) of the common features in the at least two frames and determines the change (or offset) in xy position from one frame to the next as well as the direction of movement. Thenoptical flow algorithm512 generates a velocity vector for each common feature, which represents the movement of the feature from one frame to the next frame. The results of the optical flow calculation ofoptical flow algorithm512 may be saved in optical flow outputs516.

Optical flow outputs516 may include the raw data processed byoptical flow algorithm512 and/or graphical representations of the raw data. Optical flow outputs516 may be stored inlocal memory212. Additionally, in order to provide other information that may be useful in combination with the optical flow-based dead reckoning process, the information inoptical flow outputs516 may be tagged with actuation-based timestamps fromactuation system216. These actuation-based timestamps are useful to indicate whenspray material120 is dispensed during dispensing operations with respect to the optical flow. For example, the information inoptical flow outputs516 may be tagged with timestamps for each actuation-on event and each actuation-off event ofactuation system216. More details of an exampleoptical flow output516 ofoptical flow algorithm512 are described with reference toFIG. 6.

Certain input devices

218 may be used in combination withoptical flow algorithm512 for providing information that may improve the accuracy of the optical flow calculation. In one example, a range finding device, suchsonar range finder326, may be used for determining the distance between thecamera system410 and the target surface. Preferably,sonar range finder326 is mounted in about the same plane as the FOV of the one or more digital video cameras. Therefore,sonar range finder326 may measure the distance between the one or more digital video cameras and the target surface. The distance measurement fromsonar range finder326 may support a distance input parameter ofoptical flow algorithm512, which is useful for accurately processingimage data514.

In another example, in place of or in combination withsonar range finder326, two digital video cameras may be used to perform a range finding function, which is to determine the distance between a certain digital video camera and the target surface to be sprayed. More specifically, two digital video cameras may be used to perform a stereoscopic (or stereo vision) range finder function, which is well known. For range finding, the two digital video cameras are preferably a certain optimal distance apart and the two FOVs have an optimal percent overlap (e.g., 50%-66% overlap). In this scenario, the two digital video cameras may or may not be mounted in the same plane.

In yet another example,IMU328 may be used for determining the orientation and/or angle of digital video cameras with respect to the target surface. An angle measurement fromIMU328 may support an angle input parameter ofoptical flow algorithm512, which is useful for accurately processingimage data514.

Further, when performing the optical flow-based dead reckoning process, geo-location data fromlocation tracking system310 may be used for capturing the starting position of enhancedmobile dispensing device100.

Referring toFIG. 6, an example of anoptical flow plot600 that represents the path taken by enhancedmobile dispensing device100 per the optical flow-based dead reckoning process is presented. In order to provide context,optical flow plot600 is overlaid atop, for example, a top down view of a dispensing operations jobsite610. Depicted in dispensing operations jobsite610 is abuilding612, adriveway614, and alawn616.Optical flow plot600 is overlaid atopdriveway614 andlawn616.Optical flow plot600 has startingcoordinates618 and ending coordinates620.

Optical flow plot

600 indicates the continuous path taken by enhancedmobile dispensing device100 between starting coordinates618, which may be the beginning of the dispensing operation, and endingcoordinates620, which may be the end of the dispensing operation. Starting coordinates618 may indicate the position of enhancedmobile dispensing device100 when first activated upon arrival at dispensing operations jobsite610. By contrast, endingcoordinates620 may indicate the position of enhancedmobile dispensing device100 when deactivated upon departure from dispensing operations jobsite610. The optical flow-based dead reckoning process ofoptical flow algorithm512 is tracking the apparent motion of enhancedmobile dispensing device100 along its path of use from startingcoordinates618 to ending coordinates620. That is, an optical flow plot, such asoptical flow plot600, substantially mimics the path of motion of enhancedmobile dispensing device100 when in use.

Optical flow algorithm

512 generates an optical flow plot, such asoptical flow plot600, by continuously determining the xy position offset of certain groups of pixels from one frame to the next ofimage data514 of at least one digital video camera.Optical flow plot600 is an example of a graphical representation of the raw data processed byoptical flow algorithm512. Along with the raw data itself, the graphical representation, such asoptical flow plot600, may be included in the contents of theoptical flow output516 for this dispensing operation. Additionally, raw data associated withoptical flow plot600 may be tagged with timestamp information fromactuation system216, which indicates when material is being dispensed along, for example,optical flow plot600 ofFIG. 6.

An example of an optical flow-based dead reckoning process may be summarized as follows. In one example, the optical flow-based dead reckoning process may be stopped and started manually by the user. For example, the use may manually start the process upon arrival at the job site. Then manually end the process upon departure from the job site. In another example, the optical flow-based dead reckoning process may be stopped and started automatically. For example, the process begins wheneverIMU328 detects the starting motion of enhancedmobile dispensing device100 and the process ends wheneverIMU328 detects the ending motion of enhancedmobile dispensing device100.

At least one digital video camera is activated. An initial starting position is determined byoptical flow algorithm512 reading the current latitude and longitude coordinates fromlocation tracking system310 and/or by the user manually entering the current latitude and longitude coordinates using user interface130. Then optical flow-based dead reckoning process ofoptical flow algorithm512 begins. That is, certain frames ofimage data514 are tagged in real time with “actuation-on” timestamps fromactuation system216 and certain other frames ofimage data514 are tagged in real time with “actuation-off” timestamps. Next, by processingimage data514 frame by frame,optical flow algorithm512 identifies one or more visually identifiable features (or groups of features) in at least two frames, preferably multiple frames, ofimage data514.

The pixel position offset portion of the optical flow calculation is then performed for determining the pattern of apparent motion of the one or more visually identifiable features (or groups of features). In one example, the optical flow calculation that is performed byoptical flow algorithm512 uses the Pyramidal Lucas-Kanade method for performing the optical flow calculation. In the optical flow calculation, for each frame ofimage data514,optical flow algorithm512 determines and logs the xy position (in pixels) of the features of interest.Optical flow algorithm512 then determines the change or offset in the xy positions of the features of interest from frame to frame. Using distance information (i.e., height of camera from target surface) fromsonar range finder326,optical flow algorithm512 correlates the number of pixels offset to an actual distance measurement (e.g., 100 pixels=1 cm). Relative to the FOV of the source digital video camera,optical flow algorithm512 then determines the direction of movement of the features of interest. Further, an angle measurement fromIMU328 may support a dynamic angle input parameter ofoptical flow algorithm512, which is useful for accurately processingimage data514.

Next, using the pixel offsets and direction of movement of each feature of interest,optical flow algorithm512 generates a velocity vector for each feature that is being tracked from one frame to the next frame. The velocity vector represents the movement of the feature from one frame to the next frame.Optical flow algorithm512 then generates an average velocity vector, which is the average of the individual velocity vectors of all features of interest that have been identified.

Upon completion of the optical flow-based dead reckoning process and using the aforementioned optical flow calculations,optical flow algorithm512 generates anoptical flow output516 of the current video clip. In one example,optical flow algorithm512 generates a table of timestamped position offsets with respect to the initial starting position (e.g., initial is latitude and longitude coordinates). In another example,optical flow algorithm512 generates an optical flow plot, such asoptical flow plot600 ofFIG. 6.

Next, theoptical flow output516 of the current video clip is stored. In one example, the table of timestamped position offsets with respect to the initial starting position (e.g., initial latitude and longitude coordinates), an optical flow plot (e.g.,optical flow plot600 ofFIG. 6), every nth frame (every 10^thor 20^thframe) ofimage data514, and timestamped readings from any input devices116 (e.g., timestamped readings fromIMU328,sonar range finder326, and location tracking system310) are stored inoptical flow output516 atlocal memory132. Information about dispensing operations that is stored inoptical flow outputs516 may be included in electronic records of dispensing operations.

Because a certain amount of error may be accumulating in the optical flow-based dead reckoning process, the position of enhancedmobile dispensing device100 may be recalibrated at any time during the dead reckoning process. That is, the dead reckoning process is not limited to capturing and/or entering an initial starting location only. At anytime,optical flow algorithm512 may be updated with known latitude and longitude coordinates from any source.

Another process that may be performed usingimage analysis software510 in combination with thecamera system410 is a process of surface type detection. Examples of types of surfaces may include, but are not limited to, asphalt, concrete, wood, grass, dirt (or soil), brick, gravel, stone, snow, and the like. Additionally, some types of surfaces may be painted or unpainted. More than one type of surface may be present at a jobsite.

Referring again toFIG. 5,image analysis software510 may therefore include one or moresurface detection algorithms518 for determining the type of surface being sprayed and recording the surface type insurface type data520 atlocal memory212. Surface type data is another example of information that may be stored in the electronic records of dispensing operations performed using enhancedmobile dispensing devices100.

Examples ofsurface detection algorithms518 may include, but are not limited to, a pixel value analysis algorithm, a color analysis algorithm, a pixel entropy algorithm, an edge detection algorithm, a line detection algorithm, a boundary detection algorithm, a discrete cosine transform (DCT) analysis algorithm, a surface history algorithm, and a dynamic weighted probability algorithm. One reason why multiple algorithms are executed in the process of determining the type of surface being sprayed or traversed is that any given algorithm may be more or less effective for determining certain types of surfaces. Therefore, the collective output of multiple algorithms is useful for making a final determination of the type of surface being sprayed or traversed.

Because certain types of surfaces have distinctly unique colors, the color analysis algorithm (not shown) may be used to perform a color matching operation. For example, the color analysis algorithm may be used to analyze the RGB color data of certain frames ofimage data514 from digital video cameras. The color analysis algorithm then determines the most prevalent color that is present. Next, the color analysis algorithm may correlate the most prevalent color that is found to a certain type of surface.

The pixel entropy algorithm (not shown) is a software algorithm for measuring the degree of randomness of the pixels inimage data514 from digital video camera. Randomness may mean, for example, the consistency or lack thereof of pixel order in the image data. The pixel entropy algorithm measures the degree of randomness of the pixels inimage data514 and returns an average pixel entropy value. The greater the randomness of the pixels, the higher the average pixel entropy value. The lower the randomness of the pixels, the lower the average pixel entropy value. Next, the pixel entropy algorithm may correlate the randomness of the pixels to a certain type of surface.

Edge detection is the process of identifying points in a digital image at which the image brightness changes sharply (i.e., process of detecting extreme pixel differences). The edge detection algorithm (not shown) is used to perform edge detection on certain frames ofimage data514 from at least one digital video camera. In one example, the edge detection algorithm may use the Sobel operator, which is well known. The Sobel operator calculates the gradient of the image intensity at each point, giving the direction of the largest possible increase from light to dark and/or from one color to another and the rate of change in that direction. The result therefore shows how “abruptly” or “smoothly” the image changes at that point and, therefore, how likely it is that that part of the image represents an edge, as well as how that edge is likely to be oriented. The edge detection algorithm may then correlate any edges found to a certain type of surface.

Additionally, the output of the edge detection algorithm feeds into the line detection algorithm for further processing to determine the line characteristics of certain frames ofimage data514 from at least one digital video camera. Like the edge detection algorithm, the line detection algorithm (not shown) may be based on edge detection processes that use, for example, the Sobel operator. In a brick surface, lines are present between bricks; in a sidewalk, lines are present between sections of concrete; and the like. Therefore, the combination of the edge detection algorithm and the line detection algorithm may be used for recognizing the presence of lines that are, for example, repetitive, straight, and have corners. The line detection algorithm may then correlate any lines found to a certain type of surface.

Boundary detection is the process of detecting the boundary between two or more surface types. The boundary detection algorithm (not shown) is used to perform boundary detection on certain frames ofimage data514 from at least one digital video camera. In one example, the boundary detection algorithm analyzes the four corners of the frame. When the two or more corners (or subsections) indicate different types of surfaces, the frame ofimage data514 may be classified as a “multi-surface” frame. Once classified as a “multi-surface” frame, it may be beneficial to run the edge detection algorithm and the line detection algorithm. The boundary detection algorithm may analyze the two or more subsections using any image analysis processes of the disclosure for determining the type of surface found in any of the two or more subsections.

The DCT analysis algorithm (not shown) is a software algorithm for performing standard JPEG compression operation. As is well known, in standard JPEG compression operations DCT is applied to blocks of pixels for removing redundant image data. Therefore, the DCT analysis algorithm is used to perform standard JPEG compression on frames ofimage data514 from digital video camera. The output of the DCT analysis algorithm may be a percent compression value. Further, there may be unique percent compression values for images of certain types of surfaces. Therefore, percent compression values may be correlated to different types of surfaces.

The surface history algorithm (not shown) is a software algorithm for performing a comparison of the current surface type as determined by one or more or any combinations of the aforementioned algorithms to historical surface type information. In an example, the surface history algorithm may compare the surface type of the current frame ofimage data514 to the surface type information of previous frames ofimage data514. For example, if there is a question of the current surface type being brick vs. wood, historical information of previous frames ofimage data514 may indicate that the surface type is brick and, therefore, it is most likely that the current surface type is brick, not wood.

Along with a percent probability of matching, the output of each algorithm of the disclosure for determining the type of surface being marked or traversed (e.g., the pixel value analysis algorithm, the color analysis algorithm, the pixel entropy algorithm, the edge detection algorithm, the line detection algorithm, the boundary detection algorithm, the DCT analysis algorithm, and the surface history algorithm) may include a weight factor. The weight factor may be, for example, an integer value from 0-10 or a floating point value from 0-1. Each weight factor from each algorithm may indicate the importance of the particular algorithm's percent probability of matching value with respect to determining a final percent probability of matching. The dynamic weighted probability algorithm (not shown) is used to set dynamically the weight factor of each algorithm's output. The weight factors are dynamic because certain algorithms may be more or less effective for determining certain types of surfaces.

It may be beneficial to execute the pixel value analysis algorithm, the color analysis algorithm, the pixel entropy algorithm, the edge detection algorithm, the line detection algorithm, the boundary detection algorithm, the DCT analysis algorithm, and the surface history algorithm in combination in order to confirm, validate, verify, and/or otherwise support the outputs of any one or more of the algorithms.

Referring again toFIGS. 4,5, and6,image analysis software510 is not limited to performing the optical flow-based dead reckoning process and surface type detection process.Image analysis software510 may be used to perform any other processes that may be useful in the electronic record of dispensing operations.

Referring toFIG. 7, a functional block diagram of an example of a dispensingoperations system700 that includes a network of enhancedmobile dispensing devices100 is presented. More specifically, dispensingoperations system700 may include any number of enhancedmobile dispensing devices100 that are operated by, for example,respective operators710. Associated with eachoperator710 and/or enhancedmobile dispensing device100 may be anonsite computer712. Therefore, dispensingoperations system700 may include any number ofonsite computers712.

Eachonsite computer712 may be any onsite computing device, such as, but not limited to, a computer that is present in the vehicle that is being used byoperators710 in the field. For example,onsite computer712 may be a portable computer, a personal computer, a laptop computer, a tablet device, a personal digital assistant (PDA), a cellular radiotelephone, a mobile computing device, a touch-screen device, a touchpad device, or generally any device including, or connected to, a processor. Each enhancedmobile dispensing device100 may communicate via itscommunication interface214 with its respectiveonsite computer712. More specifically, each enhancedmobile dispensing device100 may transmitdevice data222 to its respectiveonsite computer712.

While an instance ofdata processing algorithm220 and/orimage analysis software510 may reside and operate at each enhancedmobile dispensing device100, an instance ofdata processing algorithm220 and/orimage analysis software510 may also reside at eachonsite computer712. In this way,device data222 and/orimage data514 may be processed atonsite computer712 rather than at enhancedmobile dispensing device100. Additionally,onsite computer712 may be processingdevice data222 and/orimage data514 concurrently to enhancedmobile dispensing device100.

Additionally, dispensingoperations system700 may include acentral server714.Central server714 may be a centralized computer, such as a central server of, for example, the spray dispensing service provider. Anetwork716 provides a communication network by which information may be exchanged between enhancedmobile dispensing devices100,onsite computers712, andcentral server714.Network716 may be, for example, any local area network (LAN) and/or wide area network (WAN) for connecting to the Internet. Enhancedmobile dispensing devices100,onsite computers712, andcentral server714 may be connected to network716 by any wired and/or wireless means.

While an instance ofdata processing algorithm220 and/orimage analysis software510 may reside and operate at each enhancedmobile dispensing device100 and/or at eachonsite computer712, an instance ofdata processing algorithm220 and/orimage analysis software510 may also reside atcentral server714. In this way,device data222 and/orimage data514 may be processed atcentral server714 rather than at each enhancedmobile dispensing device100 and/or at eachonsite computer712. Additionally,central server714 may be processingdevice data222 and/orimage data514 concurrently to enhancedmobile dispensing device100 and/oronsite computers712.

Referring again toFIGS. 1A through 7, in other embodiments of enhancedmobile dispensing device100, the built in control electronics, such ascontrol electronics132 ofFIG. 2 and controlelectronics412 ofFIG. 5, may be replaced with a portable computing device that is electrically and/or mechanically coupled to enhancedmobile dispensing device100. For example, the functions ofcontrol electronics132 and/orcontrol electronics412 may be incorporated in, for example, a mobile telephone or a PDA device that is docked to enhancedmobile dispensing device100. This embodiment provides an additional advantage of being able to move the portable computing device, which is detachable, from one enhancedmobile dispensing device100 to another.

While various inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

Some embodiments may be implemented at least in part by a computer comprising a memory, one or more processing units (also referred to herein simply as “processors”), one or more communication interfaces, one or more display units, and one or more user input devices. The memory may comprise any computer-readable media, and may store computer instructions (also referred to herein as “processor-executable instructions”) for implementing the various functionalities described herein. The processing unit(s) may be used to execute the instructions. The communication interface(s) may be coupled to a wired or wireless network, bus, or other communication means and may therefore allow the computer to transmit communications to and/or receive communications from other devices. The display unit(s) may be provided, for example, to allow a user to view various information in connection with execution of the instructions. The user input device(s) may be provided, for example, to allow the user to make manual adjustments, make selections, enter data or various other information, and/or interact in any of a variety of manners with the processor during execution of the instructions.

The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.